WO2020119301A1

WO2020119301A1 - Two-dimensional code identification method, apparatus, and device

Info

Publication number: WO2020119301A1
Application number: PCT/CN2019/114218
Authority: WO
Inventors: 陈家大
Original assignee: 阿里巴巴集团控股有限公司
Priority date: 2018-12-11
Filing date: 2019-10-30
Publication date: 2020-06-18
Also published as: CN110046529B; TW202024997A; CN110046529A; TWI726422B

Abstract

Embodiments of the present description provide a two-dimensional code identification method, apparatus, and device. The method comprises: obtaining an image to be identified; when the image to be identified comprises a two-dimensional code, detecting a specified number of angular points of the two-dimensional code in the image to be identified according to a deep learning detection algorithm; determining, according to position coordinates of the specified number of angular points, a target area where the two-dimensional code is located in the image to be identified; performing image correction on the target area to obtain a corrected image, the image correction herein comprising at least perspective transformation; and performing two-dimensional code identification on the corrected image.

Description

Two-dimensional code recognition method, device and equipment

Technical field

One or more embodiments of this specification relate to the field of image recognition, and in particular, to a two-dimensional code recognition method, device, and equipment.

Background technique

A two-dimensional code (2-dimensional bar code) is a bar code that records information by graphs distributed in a two-dimensional direction on a plane according to certain rules. Among them, QR two-dimensional code is the most common. The QR two-dimensional code has three patterns resembling "back" characters for positioning (hereinafter referred to as backword features), which are located in the upper left corner, upper right corner, and lower left corner of the two-dimensional code, respectively. The recognition method can be as follows: the image processing technology is used to search for the three linguistic features of the two-dimensional code in the image to be recognized. According to the number and location of the backtracking features, the normal image to be recognized is restored. Then through the binarization method, it is converted into a binarization lattice. Finally, the character content implied by the dot matrix is parsed according to the standard grammar of the QR code.

However, when the to-be-recognized image is not perfect, for example, the lingering feature of the two-dimensional code in the to-be-recognized image is greatly deformed, blocked, or the to-be-recognized image is a large-angle image. According to the traditional method, it is usually impossible to search for the ideal three back-word features, so that the normal image to be recognized cannot be restored, and finally the two-dimensional code cannot be recognized. Therefore, there is a need to provide a more robust two-dimensional code recognition method.

Summary of the invention

One or more embodiments of this specification describe a two-dimensional code identification method, device, and equipment, which can accurately identify two-dimensional codes in imperfect images.

In the first aspect, a two-dimensional code recognition method is provided, including:

Obtain the image to be recognized;

When the two-dimensional code is included in the image to be recognized, the specified number of corner points of the two-dimensional code are detected in the image to be recognized according to the deep learning detection algorithm;

Determine the target area where the two-dimensional code is located in the image to be identified according to the position coordinates of the specified number of corner points;

Performing image correction on the target area to obtain a corrected image; the image correction includes at least a perspective transformation;

Perform two-dimensional code recognition on the corrected image.

In a second aspect, a two-dimensional code identification device is provided, including:

An obtaining unit, used to obtain an image to be recognized;

The detection unit is configured to detect a specified number of corner points of the two-dimensional code in the image to be recognized according to a deep learning detection algorithm when the image to be recognized acquired by the acquisition unit includes a two-dimensional code ;

A determining unit, configured to determine a target area where the two-dimensional code is located in the image to be identified according to the position coordinates of the specified number of corner points detected by the detecting unit;

A correction unit, configured to perform image correction on the target area determined by the determination unit to obtain a corrected image; the image correction includes at least perspective transformation;

The identification unit is used for performing two-dimensional code identification on the image corrected by the correction unit.

In a third aspect, a two-dimensional code identification device is provided, including:

Memory

One or more processors; and

One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and when the programs are executed by the processors, the following steps are implemented:

Obtain the image to be recognized;

Performing image correction on the target area to obtain a corrected image; the image correction includes at least perspective transformation;

Perform two-dimensional code recognition on the corrected image.

The two-dimensional code recognition method, device and equipment provided in one or more embodiments of this specification acquire images to be recognized. When the two-dimensional code is included in the image to be recognized, the specified number of corner points of the two-dimensional code are detected in the image to be recognized according to the deep learning detection algorithm. According to the position coordinates of the specified number of corner points, the target area where the two-dimensional code is located in the image to be identified is determined. Perform image correction on the target area to obtain the corrected image. The image correction here may include at least perspective transformation. Perform two-dimensional code recognition on the corrected image. It can be seen from this that the scheme provided in this specification, before recognizing the two-dimensional code, first determines the two-dimensional code area in the image to be recognized based on the deep learning detection algorithm. After that, the two-dimensional code area is corrected and recognized. In this way, accurate recognition of the two-dimensional code in the imperfect image can be achieved, and in addition, the recognition efficiency of the two-dimensional code can be greatly improved.

BRIEF DESCRIPTION

In order to more clearly explain the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present specification. Those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

Figure 1 is a schematic diagram of the two-dimensional code recognition system provided by this specification;

2 is a flowchart of a two-dimensional code recognition method provided by an embodiment of the present specification;

Figure 3 is a schematic diagram of the two-dimensional code provided by this specification;

4 is a schematic diagram of an enlargement process of an area to be identified provided by this specification;

5 is a schematic diagram of corners of a two-dimensional code provided by this specification;

Figure 6a is a schematic diagram of the corrected image provided by this specification;

6b is a schematic diagram of a contrast-enhanced image provided by this specification;

6c is a schematic diagram of a binary image provided by the specification;

7 is a schematic diagram of a two-dimensional code recognition device provided by an embodiment of the present specification;

8 is a schematic diagram of a two-dimensional code identification device provided by an embodiment of the present specification.

detailed description

The solution provided in this specification will be described below in conjunction with the drawings.

Before describing the solution provided in this specification, the invention of the solution will be introduced as follows:

Due to the influence of factors such as camera position, distance, angle, ambient lighting, etc., the acquired image of the two-dimensional code to be recognized (referred to as the image to be recognized for short) is usually not a perfect image. In order to adapt to images of various qualities to be recognized, traditional methods usually design complex multi-feature fusion logic, that is, the complexity of traditional two-dimensional code recognition methods is usually relatively high. The QR code here can refer to PDF417 QR code, Datamatrix QR code and QR QR code. In the following description of this specification, a QR two-dimensional code is used as an example for description.

The applicant of the present application considered that the reason why the traditional two-dimensional code recognition method is complicated is that the image to be recognized is not perfect enough. If the image to be recognized can be corrected well before being recognized, the complexity of the two-dimensional code recognition method will be greatly reduced. Therefore, this solution is mainly proposed for the preliminary processing flow (framework) of the image to be recognized.

First, because the present application is based on a deep learning detection algorithm to determine the two-dimensional code area of the image to be recognized, and then correct and identify the two-dimensional code area. The deep learning detection algorithm is a relatively computationally intensive algorithm, so it is necessary to minimize unnecessary input. One implementation idea may be: first determine whether the image to be recognized contains a two-dimensional code, and then input the deep learning detection algorithm only when it is determined that the image contains a two-dimensional code.

In an implementation manner, it is possible to detect a back word feature with relatively high confidence in the image to be recognized. If a character with high confidence is detected, it can be judged that the image to be recognized contains a two-dimensional code. The process of determining the confidence of the above-mentioned back word feature may be as follows: the center point of the back word feature is used as a starting point, and a number of pixels are extended around it to obtain a regular rectangular area containing the back word feature. The gray histogram statistics are performed on the regular rectangular area. If the statistical gray-scale histogram is a bimodal histogram, the confidence of the Huizi feature is relatively high, otherwise the confidence of the Huizi feature is relatively low.

In another implementation manner, an attempt can be made to detect 3 ideal linguistic features in the image to be recognized. If the 3 ideal linguistic features are detected, it can also be determined that the image to be recognized contains a two-dimensional code.

Secondly, picture correction is usually a time-consuming process, and because we are ultimately trying to recognize the two-dimensional code in the recognized image, in order to speed up the correction efficiency of the image to be recognized, we can consider only the two Dimension code area for correction. So how to determine and recognize the two-dimensional code area from the image to be recognized?

One implementation idea may be that, according to the deep learning detection algorithm, a specified number of corner points of the two-dimensional code are detected in the image to be recognized. According to the position coordinates of the specified number of corner points, the two-dimensional code area in the image to be recognized is determined. It should be noted that the above-mentioned deep learning detection algorithm may be obtained by training a plurality of images of a specified number of corner points of a pre-calibrated two-dimensional code.

Another implementation idea may be that the two-dimensional code area is determined according to the positions of the three ideal ligature features detected in the image to be recognized.

Finally, due to the deep learning detection algorithm, the specified number of corner points of the two-dimensional code can be detected. Therefore, based on the coordinates of the specified number of corner points, image correction processing such as perspective transformation and lens distortion correction can be simultaneously performed on the two-dimensional code area. When the above-mentioned various image correction processes can be performed at the same time, multiple writing of image data in the memory can be avoided, which can greatly improve the correction efficiency of the image, thereby further improving the recognition efficiency of the two-dimensional code.

It can be understood that, after the image to be recognized has undergone the above-mentioned series of preliminary processing, the quality of the image to be recognized can be greatly improved, so that subsequent recognition algorithms can more easily recognize the content contained in the two-dimensional code.

Based on the above inventive concept, the solution provided in this description can be obtained. The solutions provided in this specification are described in detail below.

FIG. 1 is a schematic diagram of a two-dimensional code recognition system provided by this specification. In FIG. 1, the two-dimensional code recognition system 10 may include: a feature detection module 102, a corner detection module 104, an image correction module 106, and a recognition module 108.

The feature detection module 102 is used to detect a back word feature with relatively high confidence in the image to be recognized. The hyphen feature here has the following characteristics: the length ratio of line segments composed of black and white pixels is 1:1:3:1:1. Using this feature, the Huizi feature can be recognized in the image to be recognized. The determination of the confidence level of the above-mentioned back word feature is as described above and will not be repeated here.

The corner detection module 104 is configured to detect a specified number of corners of the two-dimensional code in the image to be recognized including the two-dimensional code. The to-be-recognized image containing the two-dimensional code here may refer to the to-be-recognized image that has detected a character with high confidence. As mentioned above, the corner detection module 104 can specifically detect the specified number of corners of the two-dimensional code through a deep learning detection algorithm.

The image correction module 106 is used to perform image correction on the area (that is, the two-dimensional code area) determined by the position coordinates of the specified number of corner points. The image correction here may include but is not limited to perspective transformation and lens distortion correction. It should be noted that, due to the deep learning detection algorithm, a specified number of corner points of the two-dimensional code can be detected. Therefore, based on the specified number of corner points, two-dimensional code regions can be simultaneously subjected to perspective transformation and lens distortion correction, which can greatly improve the efficiency of image correction.

The recognition module 108 is used to recognize the two-dimensional code area after image correction. For example, identify and output the content contained in the QR code.

Optionally, the above two-dimensional code recognition system may further include a contrast enhancement module 110. The contrast enhancement module 110 is used to enhance the contrast of the image-corrected two-dimensional code region by using a local histogram method, so that a better contrast can be obtained.

In addition, a binarization module 112 may also be included. The binarization module 112 is used to perform binarization processing on the image-corrected two-dimensional code region or the contrast-enhanced two-dimensional code region, thereby making the two-dimensional code region easier to recognize.

2 is a flowchart of a two-dimensional code recognition method provided by an embodiment of the present specification. The execution subject of the method may be a device with processing capabilities: a server or a system or an apparatus, for example, it may be a two-dimensional code recognition system in FIG. 1. As shown in FIG. 2, the method may specifically include:

Step 202: Acquire an image to be recognized.

Here, the image to be recognized may be obtained through the camera of the terminal device, and the terminal device here may refer to a smartphone, tablet computer, digital camera, or other similar terminal device. After acquiring the image to be recognized, the image to be recognized may be processed in grayscale to obtain a grayscale image. It should be noted that the value range of the gray value (referred to as the pixel value) of the pixels in the gray image may be: [0,255].

As described above, in order to reduce unnecessary input of the deep learning detection algorithm, after obtaining the grayscale image, the following determination step of whether a two-dimensional code is included may be performed. This step may be specifically performed by the feature detection module 102, which may specifically include:

Step a: Perform feature detection on the gray-scale image to detect whether the image to be recognized contains linguistic features.

As mentioned above, the syllable feature in this specification has the characteristics of 1:1:3:1:1, so the aforementioned syllable feature can be detected based on this feature. It should be noted that, in the case where the image to be recognized is relatively perfect, it is usually possible to detect three back features. However, when the ligature features in the image to be recognized appear deformed, occluded, or the image to be recognized is a large-angle image, the ideal three ligature features cannot be detected, but a single ligature feature can usually be detected. The method for judging whether a two-dimensional code is included in the embodiment of the specification has high robustness. Taking the two-dimensional code shown in FIG. 3 as an example, the back word feature in the upper left corner can be detected.

In step b, if the lingering feature is detected, the center point of the lingering feature is used as a starting point, and a number of pixels are expanded to the surrounding area to obtain a regular rectangular area containing the lingering feature.

Here, "surrounding" may refer to the four directions of the back character feature, so the above expansion operation is to extend several pixels in the four directions of the back character feature, respectively. Among them, the number of pixels expanded in each direction is determined by the size of the back word feature. Specifically, according to the above characteristics of 1:1:3:1:1, it can be known that the ligature feature in this specification may include 7*7 dot matrix units. Assuming that 1 dot matrix unit corresponds to 1 pixel, then return The size of the word feature is: 1*7=7 pixels. In an implementation manner, the number of the extended pixels may be: 1*8, which is set to 8 here because the final rectangular area obtained needs to include a back word feature (that is, greater than 7 dot-matrix units), The 1 here represents the aforementioned 1 pixel. Of course, in practical applications, the 8 in the formula can also be replaced by any number greater than 8, which is not limited in this manual.

Step c: Perform gray histogram statistics on the positive rectangular area.

The horizontal coordinate of the grayscale histogram here can be different pixel values contained in the regular rectangular area. As mentioned above, the value range of the pixel value here is: [0,255], and the vertical coordinate is the number of different pixel values .

Step d: If the statistical grayscale histogram is a bimodal histogram, it is determined that the image to be recognized contains a two-dimensional code.

It should be noted that, the above steps b-d may be performed when the ideal three syllable features cannot be detected. If three ideal linguistic features can be detected through step a, it can be directly judged that the image to be recognized contains a two-dimensional code without performing steps b-d, which is not limited in this specification.

In this embodiment of the present specification, a method for determining whether a two-dimensional code is included in an image to be recognized by detecting a single linguistic feature with high confidence can reduce the misrecognition rate of whether a two-dimensional code is included.

Step 204: When the image to be recognized contains a two-dimensional code, detect the specified number of corner points of the two-dimensional code in the image to be recognized according to the deep learning detection algorithm.

Here, it may mean that the corner detection module 104 detects the specified number of corners of the two-dimensional code in the image to be recognized according to the deep learning detection algorithm.

Optionally, in order to ensure that when the image to be recognized contains a two-dimensional code, the deep learning detection algorithm can detect the specified number of corners of the two-dimensional code, before performing step 204 in this embodiment of the present specification, the following two may also be performed: The judgment steps of the dimension code include:

Get the size of the back word feature. Convert the size of the QR code according to the preset conversion rules and the size of the back word feature. If the size of the two-dimensional code does not satisfy the preset condition, the region to be recognized centered on the character back is extracted from the image to be recognized. Enlarge the area to be identified.

The conversion process of the size of the above two-dimensional code can be exemplified as follows: Suppose that the size of the obtained back word feature is: 3*7=21 pixels, so that it can be determined that one dot unit of the back word feature corresponds to 3 pixels. And it is assumed that the preset conversion rule is: the size of the two-dimensional code is determined according to the number of pixels corresponding to one lattice unit and the preset maximum two-dimensional code lattice. Then, when the preset maximum two-dimensional code lattice is 57*57, the size of the two-dimensional code may be: 3*57=171 pixels.

Of course, in practical applications, the above-mentioned preset conversion rule can also be set to other algorithms, for example, the size of the back word feature is enlarged by a preset multiple to determine the size of the two-dimensional code, which is not limited in this specification.

FIG. 4 shows a schematic diagram of an enlargement process of an area to be recognized. In FIG. 4, assuming that the size of the image to be recognized is: 1000*1000, and assuming that the size of the converted two-dimensional code does not meet the preset conditions according to the above conversion rule, the character-return feature can be extracted from the image to be recognized as The size of the area to be recognized in the center may be 400*400, and then the 400*400 area to be recognized is enlarged.

It can be understood that when the enlargement operation of the area to be recognized is also performed, step 204 may be replaced by: detecting the specified number of corner points of the two-dimensional code in the enlarged area to be recognized according to the deep learning detection algorithm.

In one example, the specified number of corner points in step 204, or in the step after the replacement may refer to 4 corner points of the two-dimensional code. Taking FIG. 3 as an example, the detected four corner points may be as shown in FIG. 5.

In addition, the deep learning detection algorithm in this specification may be obtained by training a plurality of images of a specified number of corner points of a pre-calibrated two-dimensional code. By training the deep learning detection algorithm, the human eye's ability to perceive the corners of the two-dimensional code can be simulated, thereby obtaining higher robustness. When new scenes appear, the algorithm can also be updated faster through deep learning fine tuning.

It can be seen from this that the single-word-return feature detected with high confidence in this specification can be used not only to determine whether the image to be recognized contains a two-dimensional code, but also to convert the size of the two-dimensional code. When the size of the two-dimensional code does not meet the preset condition, the surrounding area of the back word feature can be enlarged, thereby improving the success rate of detecting the corner points of the two-dimensional code. In addition, the enlarged area can also be understood as the coarse positioning of the two-dimensional code, and this coarse positioning method can reduce the search space of the deep learning detection algorithm.

Furthermore, the embodiment of this specification uses a deep learning detection algorithm to determine the two-dimensional code area compared with the traditional method (that is, based on three ideal ligature features to locate the two-dimensional code area), the method has better Greatness. Specifically, the deep learning detection algorithm provided by the embodiments of the present specification can accurately locate the two-dimensional code area even when the traverse feature of the two-dimensional code is deformed, blocked, or the image to be recognized is a large-angle image.

Step 206: Determine the target area where the two-dimensional code is located in the image to be recognized according to the position coordinates of the specified number of corner points.

Taking FIG. 5 as an example, the target area determined in this step may be a rectangular area composed of four corner points in the figure.

Step 208: Perform image correction on the target area to obtain a corrected image.

For example, the image correction module 108 may perform the above steps 206 and 208.

Taking FIG. 5 as an example, after image correction is performed on the target area, the corrected image as shown in FIG. 6a can be obtained.

The above image correction may include at least perspective transformation. In addition, lens distortion correction can also be included. It should be noted that, since step 4 can already determine the four corner points of the target area, this step can directly perform perspective transformation. There is no need to perform lens distortion correction on the target area to determine the four corner points of the target area, and then perform perspective transformation on it. In one implementation, when lens distortion correction is also performed on the target area, the lens distortion correction and perspective transformation can be performed simultaneously, that is, only image data needs to be written to the memory once, thereby greatly improving the efficiency of image correction .

In addition, since the lens distortion correction is a non-linear change, it is very resource intensive. Therefore, in this step, only the image correction is performed on the target area, and the image correction is not performed on the entire image to be recognized, which can greatly reduce the amount of calculation.

Step 210, perform two-dimensional code recognition on the corrected image.

For example, the recognition module 108 may perform two-dimensional code recognition on the corrected image.

In order to make the corrected image easier to recognize, the embodiments of the present specification may also perform image processing steps such as contrast enhancement and binarization on the corrected image. Specifically, the local histogram method is first used to perform contrast enhancement processing on the corrected image to obtain a contrast enhanced image. Then, the enhanced image is binarized to obtain a binarized image. Finally, two-dimensional code recognition is performed on the binary image.

Taking FIG. 6a as an example, after performing contrast enhancement processing on it, a contrast enhanced image as shown in FIG. 6b can be obtained. After that, when the image shown in FIG. 6b is binarized, the binarized image shown in FIG. 6c can be obtained.

In summary, the two-dimensional code recognition method provided by the embodiment of the present specification determines whether the two-dimensional code is included in the image to be recognized by detecting a single ligature feature with high confidence. Discard the to-be-recognized images that do not contain the two-dimensional code, so that all images can be prevented from going through a deep learning detection algorithm that is relatively computationally intensive. In addition, through the single-word-back feature with high confidence, coarse positioning of the two-dimensional code can also be achieved, so that when the size of the two-dimensional code does not meet the preset conditions, the back-characteristic feature is used as the center to determine the surrounding area. To zoom in. Furthermore, by training the deep learning detection algorithm to locate the corners of the two-dimensional code, it is possible to avoid the complex logic of multi-feature fusion designed by the traditional algorithm in order to adapt to the image quality of various two-dimensional codes. Finally, based on the corners detected by the deep learning detection algorithm, image correction processing such as perspective change and lens distortion correction can be simultaneously performed on the two-dimensional code area, thereby greatly improving the image correction efficiency.

Corresponding to the above two-dimensional code recognition method, an embodiment of this specification also provides a two-dimensional code recognition device. As shown in FIG. 7, the device may include:

The obtaining unit 702 is used to obtain an image to be recognized.

The detection unit 704 is configured to detect a specified number of corner points of the two-dimensional code in the image to be recognized according to the deep learning detection algorithm when the image to be recognized acquired by the acquisition unit 702 includes the two-dimensional code.

The function of the detection unit 704 can be realized by the corner detection module 104.

The determining unit 706 is configured to determine the target area where the two-dimensional code is located in the image to be identified according to the position coordinates of the specified number of corner points detected by the detecting unit 704.

The correction unit 708 is configured to perform image correction on the target area determined by the determination unit 706 to obtain a corrected image. The image correction here may include at least perspective transformation. In addition, lens distortion correction can also be included.

The functions of the determination unit 706 and the correction unit 708 described above may be implemented by the image correction module 106.

The recognition unit 710 is configured to perform two-dimensional code recognition on the image corrected by the correction unit 708.

The function of the identification unit 710 can be implemented by the identification module 108.

Optionally, the device may further include: a judging unit (not shown in the figure), which is used to perform feature detection on the image to be recognized to detect whether the image to be recognized contains a back word feature. If the backtrack feature is detected, the center point of the backtrack feature is used as the starting point, and a number of pixels are extended around it to obtain a regular rectangular area containing the backtrack feature. The gray histogram statistics are performed on the regular rectangular area. If the statistical grayscale histogram is a bimodal histogram, it is determined that the image to be recognized contains a two-dimensional code.

The function of the above-mentioned judgment unit can be realized by the feature detection module 102.

Optionally, the device may further include: a conversion unit, an extraction unit, and an amplification unit.

The obtaining unit 702 is also used to obtain the size of the back word feature.

The conversion unit is configured to convert the size of the two-dimensional code according to a preset conversion rule and the size of the back word feature acquired by the acquisition unit 702.

The extracting unit is used to extract the region to be recognized centered on the character-returning feature from the image to be recognized if the size of the two-dimensional code converted by the conversion unit does not satisfy the preset condition.

An enlargement unit is used to enlarge the region to be recognized extracted by the extraction unit.

The detection unit 704 is specifically used for:

According to the deep learning detection algorithm, a specified number of corner points of the two-dimensional code are detected in the enlarged area to be recognized.

The identification unit 710 is specifically used for:

The method of local histogram is used to perform contrast enhancement processing on the corrected image to obtain a contrast enhanced image.

Binarize the contrast-enhanced image to obtain a binary image.

Perform two-dimensional code recognition on the binary image.

The function of the recognition unit 710 here can be realized by the above-mentioned recognition module 108, contrast enhancement module 110, and binarization module 112 together.

The functions of the functional modules of the device in the above embodiments of the present specification can be implemented through the steps of the above method embodiments. Therefore, the specific working process of the device provided by an embodiment of the present specification will not be repeated here.

In a two-dimensional code recognition device provided by an embodiment of this specification, the obtaining unit 702 obtains an image to be recognized. When the two-dimensional code is included in the image to be recognized, the detection unit 704 detects the specified number of corner points of the two-dimensional code in the image to be recognized according to the deep learning detection algorithm. The determining unit 706 determines the target area where the two-dimensional code is located in the image to be recognized according to the position coordinates of the specified number of corner points. The correction unit 708 performs image correction on the target area to obtain a corrected image. The image correction here may include at least perspective transformation. The recognition unit 710 performs two-dimensional code recognition on the corrected image. As a result, accurate recognition of the two-dimensional code in the imperfect image can be achieved, and in addition, the recognition efficiency of the two-dimensional code can be greatly improved.

Corresponding to the above two-dimensional code recognition method, an embodiment of this specification also provides a two-dimensional code recognition device, as shown in FIG. 8, the device may include: a memory 802, one or more processors 804, and one or more Programs. The one or more programs are stored in the memory 802, and are configured to be executed by one or more processors 804, and when the program is executed by the processor 804, the following steps are implemented:

Acquire the image to be recognized.

When the image to be recognized contains a two-dimensional code, according to the deep learning detection algorithm, the specified number of corner points of the two-dimensional code are detected in the image to be recognized.

According to the position coordinates of the specified number of corner points, the target area where the two-dimensional code is located in the image to be identified is determined.

Perform image correction on the target area to obtain a corrected image. The image correction includes at least perspective transformation.

Perform two-dimensional code recognition on the corrected image.

The two-dimensional code recognition device provided by an embodiment of the present specification can realize accurate recognition of the two-dimensional code in imperfect images.

The embodiments in this specification are described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method embodiment.

The steps of the method or algorithm described in conjunction with the disclosure of the present specification may be implemented in hardware, or may be implemented by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, mobile hard disk, CD-ROM or any other form of storage known in the art Medium. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may be located in the ASIC. In addition, the ASIC may be located in the server. Of course, the processor and the storage medium may also exist as discrete components in the server.

Those skilled in the art should realize that in one or more of the above examples, the functions described in the present invention may be implemented by hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media and communication media, where communication media includes any medium that facilitates transfer of a computer program from one place to another. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

The foregoing describes specific embodiments of the present specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order shown or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of this specification in detail. It should be understood that the above descriptions are only specific implementations of this specification and are not intended to limit the scope of this specification. The scope of protection, any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of this specification, shall be included in the scope of protection of this specification.

Claims

A two-dimensional code recognition method, including:

Obtain the image to be recognized;

When the two-dimensional code is included in the image to be recognized, the specified number of corner points of the two-dimensional code are detected in the image to be recognized according to the deep learning detection algorithm;

Determine the target area where the two-dimensional code is located in the image to be identified according to the position coordinates of the specified number of corner points;

Performing image correction on the target area to obtain a corrected image; the image correction includes at least perspective transformation;

Perform two-dimensional code recognition on the corrected image.
The method according to claim 1, further comprising the step of determining whether the two-dimensional code is included in the image to be recognized, including:

Performing feature detection on the image to be recognized, to detect whether the image to be recognized contains a back word feature;

If the back word feature is detected, the center point of the back word feature is used as a starting point, and a number of pixels are extended around it to obtain a regular rectangular area containing the back word feature;

Perform gray histogram statistics on the regular rectangular area;

If the statistical gray-scale histogram is a bimodal histogram, it is determined that the two-dimensional code is included in the image to be recognized.
The method according to claim 2, before detecting the specified number of corner points of the two-dimensional code in the image to be recognized according to the deep learning detection algorithm, further comprising:

Obtain the size of the back word feature;

Convert the size of the two-dimensional code according to a preset conversion rule and the size of the back word feature;

If the size of the two-dimensional code does not satisfy the preset condition, extract the region to be recognized centered on the character back feature from the image to be recognized;

Enlarge the area to be identified;

The detecting the specified number of corner points of the two-dimensional code in the image to be recognized according to the deep learning detection algorithm includes:

According to the deep learning detection algorithm, a specified number of corner points of the two-dimensional code are detected in the enlarged area to be recognized.
The method of claim 1, the image correction further comprising lens distortion correction.
The method according to any one of claims 1 to 4, wherein the two-dimensional code recognition of the corrected image includes:

Adopting a local histogram method to perform contrast enhancement processing on the corrected image to obtain a contrast enhanced image;

Performing binary processing on the contrast-enhanced image to obtain a binary image;

Perform two-dimensional code recognition on the binary image.
A two-dimensional code recognition device, including:

An obtaining unit, used to obtain an image to be recognized;

The detection unit is configured to detect a specified number of corner points of the two-dimensional code in the image to be recognized according to a deep learning detection algorithm when the image to be recognized acquired by the acquisition unit includes a two-dimensional code ;

A determining unit, configured to determine a target area where the two-dimensional code is located in the image to be identified according to the position coordinates of the specified number of corner points detected by the detecting unit;

A correction unit, configured to perform image correction on the target area determined by the determination unit to obtain a corrected image; the image correction includes at least perspective transformation;

The identification unit is used for performing two-dimensional code identification on the image corrected by the correction unit.
The apparatus according to claim 6, further comprising: a judging unit, configured to perform feature detection on the image to be recognized to detect whether the image to be recognized contains a back word feature;

If the back word feature is detected, the center point of the back word feature is used as a starting point, and a number of pixels are extended around it to obtain a regular rectangular area containing the back word feature;

Perform gray histogram statistics on the regular rectangular area;

If the statistical gray-scale histogram is a bimodal histogram, it is determined that the two-dimensional code is included in the image to be recognized.
The device according to claim 7, further comprising: a conversion unit, an extraction unit and an amplification unit;

The acquiring unit is also used to acquire the size of the back word feature;

The conversion unit is configured to convert the size of the two-dimensional code according to a preset conversion rule and the size of the back word feature acquired by the acquisition unit;

The extracting unit is configured to extract, from the image to be recognized, a region to be recognized centered on the syllable feature from the image to be recognized if the size of the two-dimensional code converted by the conversion unit does not satisfy a preset condition;

The enlargement unit is used to enlarge the region to be recognized extracted by the extraction unit;

The detection unit is specifically used for:

According to the deep learning detection algorithm, a specified number of corner points of the two-dimensional code are detected in the enlarged area to be recognized.
The apparatus of claim 6, the image correction further comprises lens distortion correction.
The apparatus according to any one of claims 6-9, the identification unit is specifically configured to:

Adopting a local histogram method to perform contrast enhancement processing on the corrected image to obtain a contrast enhanced image;

Performing binary processing on the contrast-enhanced image to obtain a binary image;

Perform two-dimensional code recognition on the binary image.
A two-dimensional code identification device, including:

Memory

One or more processors; and

One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and when the programs are executed by the processors, the following steps are implemented:

Obtain the image to be recognized;

When the two-dimensional code is included in the image to be recognized, the specified number of corner points of the two-dimensional code are detected in the image to be recognized according to the deep learning detection algorithm;

Determine the target area where the two-dimensional code is located in the image to be identified according to the position coordinates of the specified number of corner points;

Performing image correction on the target area to obtain a corrected image; the image correction includes at least perspective transformation;

Perform two-dimensional code recognition on the corrected image.